Trust, with various components and derivatives thereof, plays a critical role in the collection, synthesis and interpretation of data, in a network. The collected, synthesized and interpreted data is transformed to knowledge that may aid in decision making process in various sectors such as defense, military, and the like.
Specifically, the various components and derivatives of the trust are derived from different contexts reflecting the intricacies of interactions among social-cognitive, information, and communication networks. As such, there is much complexity in fully understanding the different contexts as well as interdependency among the social-cognitive, information, and communication networks. Therefore, developing a composite notion of the trust and evaluating an impact thereof are fundamental to fully understand the different contexts and the interdependency, as the ability to achieve trust may greatly enhance effectiveness of the decision making process.
Accordingly, the role that the trust plays within the different contexts, such as in information networks, within a Tasking, Collection, Processing, Exploitation, and Dissemination (TCPED) chain is required to be investigated. In such different contexts, the composite notion of the trust as that of a trust component is derived and interpreted. Thereafter, the derived composite notion of the trust is tied to a particular stage of the TCPED chain. The TCPED chain is presented to a human or an automatic decision maker and decisions are then made based on a level of trust related to the received information in the particular stage of the TCPED chain. Further, due to sweeping complexities of evaluating the composite trust, data collection and decision fusion aspects of the TCPED chain are described.
The first stage in the TCPED chain is tasking. At this stage, various information (data) sources are mapped according to information needs and tasked with data collection. Suitable examples of the various information (data) sources include, but are not limited to, sensors (nodes), people, database queries, and the like. Further, ascertaining the authenticity of the various information (data) sources is important. For example, to ascertain the authenticity of the various information (data) sources some critical questions that act as indicators, such as whether the sensors (nodes) generating the data are trustworthy, may be evaluated. The track record of the sensors (nodes) generating the data may also be evaluated. Another question to be evaluated is whether there is a major deviation in behavior of the sensors (nodes) generating the data in the past. Any of the aforementioned indicators, if measured correctly, enable to determine a degree that the data generated may be trusted. With the completion of tasking stage, the data is collected, transported, and fused.
At such a stage, malicious entities may impact validity of the data generated by either replacing the data entirely or inserting faulty information (data). In addition, complexity of the data collection process is affected by underlying communication infrastructure of sensor (nodes) networks. Typically, the data generated is fused through a hierarchy of fusion subsystems. As such, the trust of information also includes trust of the fusion subsystems as well as the communication infrastructure having communication links connecting the fusion subsystems. Accordingly, the trust of information may be derived from the trust in the tasking, the information (data) sources, fusion hierarchy, the communication links, and the like. Therefore, the trust management for information fusion takes into account the various components and the different contexts. Further, information/data fusion exploits the synergy among the raw data and converts the raw data to knowledge to facilitate the decision making.
Various studies have been conducted to understand the sensor applications, including methods, algorithms, models, and architectures. Further, attempts have been made to understand/study concepts including information fusion for wireless sensor networks, optimal data fusion in multiple sensor detection systems, fusion of decisions transmitted over fading channels in wireless sensor networks, decision fusion rules in wireless sensor networks using fading channel statistics, learning the quality of sensor data in distributed decision fusion, and the like. However, in most of the aforementioned studies, all sensors are assumed to be “well-behaved”, and the effect of compromised sensors on the fusion process is not considered.
It is also to be understood that the definitions of trust vary over different disciplines, e.g., computer science vs. social sciences, and the like. From the perspective of social science, the concept of trust centers on expectation of benevolent behaviors from others. Further, for a cross discipline view of trust, the expectation comes from interactions with trustees (ones to be trusted) over time. Such interactions allow the assessment of the consistency or discrepancy between expected and observed behaviors. Many recent studies involve works, such as with regard to trust models and trust evaluation metrics for ad-hoc networks, trust evaluation in anarchy for autonomous networks, and reputation-based framework for high integrity sensor networks. Further, aspects such as distributed reputation system for tracking applications in sensor networks that draw inspirations from the social aspects of trust and apply trust related concepts and methodologies to enhance the system integrity of peer-to-peer (P2P), mobile ad-hoc networks, sensor networks, and pervasive computing networks, have been studied. However, studies confined to trust models and trust evaluation metrics for ad-hoc networks focus on the evaluation of trust evidence in ad-hoc models. The evaluation process is modeled as a path problem on a directed graph, with nodes representing entities and edges representing trust relations. Further, two nodes in such models may establish an indirect trust relation without previous direct interaction. Furthermore, studies confined to trust evaluation in anarchy for autonomous networks involve analysis of the impact of distributed trust management—local voting rule on the structure and behavior of autonomous network. In addition, studies relating to reputation-based framework for high integrity sensor networks, and distributed reputation system for tracking applications in sensor networks, adopt a reputation system approach to mitigate the performance degradation caused by malicious nodes. Specifically, for the distributed reputation system for tracking applications in sensor networks, a reputation assignment mechanism has been developed for multi-object tracking application.
Based on the foregoing, most of the studies intent to focus a specific aspect of decision fusion in a network without considering the effect of compromised sensors, and fading and noisy communication channels; and addressing the threats originating due to the presence of the compromised sensors, and the fading and noisy communication channels for decision fusion.
Accordingly, there exists a need for efficiently and effectively managing trust for decision fusion within a network while considering the effect of compromised sensors, and fading and noisy communication channels, on the decision fusion; and addressing the threats of the compromised sensors, and the fading and noisy communication channels, in order to improve performance of a fusion process within the network.